Considering the balance of performance and efficiency, sampled point and
voxel methods are usually employed to down-sample dense events into sparse
ones. After that, one popular way is to leverage a graph model which treats the
sparse points/voxels as nodes and adopts graph neural networks (GNNs) to learn
the representation for event data. Although good performance can be obtained,
however, their results are still limited mainly due to two issues. (1) Existing
event GNNs generally adopt the additional max (or mean) pooling layer to
summarize all node embeddings into a single graph-level representation for the
whole event data representation. However, this approach fails to capture the
importance of graph nodes and also fails to be fully aware of the node
representations. (2) Existing methods generally employ either a sparse point or
voxel graph representation model which thus lacks consideration of the
complementary between these two types of representation models. To address
these issues, in this paper, we propose a novel dual point-voxel absorbing
graph representation learning for event stream data representation. To be
specific, given the input event stream, we first transform it into the sparse
event cloud and voxel grids and build dual absorbing graph models for them
respectively. Then, we design a novel absorbing graph convolutional network
(AGCN) for our dual absorbing graph representation and learning. The key aspect
of the proposed AGCN is its ability to effectively capture the importance of
nodes and thus be fully aware of node representations in summarizing all node
representations through the introduced absorbing nodes. Finally, the event
representations of dual learning branches are concatenated together to extract
the complementary information of two cues. The output is then fed into a linear
layer for event data classification